Instructions to use rhplus0831/maid-yuzu-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rhplus0831/maid-yuzu-v8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rhplus0831/maid-yuzu-v8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rhplus0831/maid-yuzu-v8")
model = AutoModelForCausalLM.from_pretrained("rhplus0831/maid-yuzu-v8")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rhplus0831/maid-yuzu-v8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rhplus0831/maid-yuzu-v8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhplus0831/maid-yuzu-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rhplus0831/maid-yuzu-v8

SGLang

How to use rhplus0831/maid-yuzu-v8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rhplus0831/maid-yuzu-v8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhplus0831/maid-yuzu-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rhplus0831/maid-yuzu-v8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhplus0831/maid-yuzu-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rhplus0831/maid-yuzu-v8 with Docker Model Runner:
```
docker model run hf.co/rhplus0831/maid-yuzu-v8
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

maid-yuzu-v8

This is a merge of pre-trained language models created using mergekit.

v7's approach worked better than I thought, so I tried something even weirder as a test. I don't think a proper model will come out, but I'm curious about the results.

Merge Details

Merge Method

This models were merged using the SLERP method in the following order:

maid-yuzu-v8-base: mistralai/Mixtral-8x7B-v0.1 + mistralai/Mixtral-8x7B-Instruct-v0.1 = 0.5
maid-yuzu-v8-step1: above + jondurbin/bagel-dpo-8x7b-v0.2 = 0.25
maid-yuzu-v8-step2: above + cognitivecomputations/dolphin-2.7-mixtral-8x7b = 0.25
maid-yuzu-v8-step3: above + NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss = 0.25
maid-yuzu-v8-step4: above + ycros/BagelMIsteryTour-v2-8x7B = 0.25
maid-yuzu-v8: above + smelborp/MixtralOrochi8x7B = 0.25

Models Merged

The following models were included in the merge:

smelborp/MixtralOrochi8x7B
../maid-yuzu-v8-step4

Configuration

The following YAML configuration was used to produce this model:

base_model:
  model:
    path: ../maid-yuzu-v8-step4
dtype: bfloat16
merge_method: slerp
parameters:
  t:
  - value: 0.25
slices:
- sources:
  - layer_range: [0, 32]
    model:
      model:
        path: ../maid-yuzu-v8-step4
  - layer_range: [0, 32]
    model:
      model:
        path: smelborp/MixtralOrochi8x7B

Downloads last month: 10

Safetensors

Model size

47B params

Tensor type

BF16

Model tree for rhplus0831/maid-yuzu-v8

Base model

smelborp/MixtralOrochi8x7B

Finetuned

(4)

this model

Quantizations

1 model